text: 
  class default_args():
      '''
      Default args we don't use
      '''
      def __init__(self):
          self.config_file = None # 'json configuration file'
          self.device_ids = [0]
          self.world_size = -1
          self.local_rank = -1
          self.dist_init = 'env://'
          self.dist_backend = 'nccl'
          self.workers = 8
          self.label_smoothing = 0
          self.mixup = None
          self.duplicates = 1
          self.chunk_batch = 1
          self.augment = False
          self.cutout = False
          self.calibrate_bn = False
          self.autoaugment = False
          self.avg_out = False
          self.print_freq = 10
          self.resume = ''
          self.absorb_bn = False
          self.seed = 123
          self.save = ''
          self.input_size = None
          self.dtype = 'float'
          self.optimizer = 'SGD'
          self.lr = 0.1
          self.momentum = 0.9
          self.weight_decay = 0
          self.loss_scale = 1
          self.grad_clip = -1
          self.adapt_grad_norm = None
          self.tensorwatch = False
          self.start_epoch = -1
          self.drop_optim_state = False
  
  class default_td_args(default_args):
      '''
      Default args we do use in this project
      '''
      def __init__(self):
          super().__init__()
          self.dataset = 'cifar10'
          self.model = 'resnet'
          self.device = 'cuda'
          self.model_config = ''
          self.datasets_dir = '~/Datasets'
          self.results_dir = 'results'
  
  class train_args(default_td_args):
      '''
      training args
      '''
      def __init__(self):
          super().__init__()
          self.epochs = 200
          self.batch_size = 32
          self.eval_batch_size = self.batch_size
          self.evaluate = False
          self.dropout_type = 'outliers'
          self.dropout_perc = 0.5 # percentage of things being targeted.
          self.drop_rate = 0.5 # probability to drop targeted things.
  
  class eval_prune_args(default_td_args):
      '''
      pruning args
      '''
      def __init__(self):
          super().__init__()
          self.eval_path = 'no_td_model\\checkpoint.pth.tar'
          #self.eval_path = 'td_05unit_model\\checkpoint.pth.tar'
          self.batch_size = 256
          self.pruning_perc = None
          self.pruning_percs = [0] #[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
          self.pruning_policy = 'unit'
          self.gather_histograms = True
  
  
  # Histogram comparison args
  class comparisonal_histograms_args():
      def __init__(self):
          self.path1 = 'results\\evaluating_results\\2019-10-25_10-13-44'
          self.path2 = 'results\\evaluating_results\\2019-10-25_10-19-17'
          self.csvname = 'histograms_data.csv'
          self.argfile_name = 'args.txt'